基于FAGA-BP 神经网络算法的配电网故障感知分析

李文科; 缪新萍; 孔庆波; 黄笠煌; 黄璐; 付麟淞

李文科,缪新萍,孔庆波,黄笠煌,黄璐,付麟淞.基于FAGA-BP 神经网络算法的配电网故障感知分析[J].电测与仪表,2026,63(3):168-175.
Liwenke,MIAO Xinping,KONG Qingbo,HUANG Lihuang,HUANG Lu,FU Linsong.Fault perception analysis of distribution network based on FAGA-BP neural network algorithm[J].Electrical Measurement & Instrumentation,2026,63(3):168-175.

基于FAGA-BP 神经网络算法的配电网故障感知分析

Fault perception analysis of distribution network based on FAGA-BP neural network algorithm

DOI：10.19753/j.issn1001-1390.2026.03.017

中文关键词: 故障诊断遗传算法神经网络故障定位矩阵

英文关键词:fault diagnosis, genetic algorithm, neural network, fault location matrix

基金项目:国家电网科技项目（编号066700KK52222033）

作者中文名	作者英文名	单位
李文科	Liwenke	贵州电网有限责任公司信息中心
缪新萍	MIAO Xinping	贵州电网有限责任公司信息中心
孔庆波	KONG Qingbo	贵州电网有限责任公司信息中心
黄笠煌	HUANG Lihuang	贵州电网有限责任公司信息中心
黄璐	HUANG Lu	贵州电网有限责任公司凯里天柱供电局
付麟淞	FU Linsong	贵州电网有限责任公司凯里供电局

摘要点击次数: 425

中文摘要:

针对配网线路故障定位准确率不稳定,受配电自动化装置通信影响大的问题,文中设计了基于融合告警和遗传算法的BP 神经网络算法(BP neural network algorithm based on fusion alarm and genetic algorithm, FAGA-BP)配电网故障感知分析。文中设计了故障定位感知流程,在配电自动化装置告警信号的基础上引入了配电变压器失电告警信号,建立了有源配电网的融合告警故障感知定位模型,包括配网自动化装置的保护动作过流告警信号矩阵和配变失电告警故障矩阵,对故障特征量的选取和融合告警规则进行了定义,利用定义规则将保护过流告警矩阵和失电告警矩阵生成融合告警故障特征量定位模型矩阵,制定了故障类型编码表以表征配网区段故障类型,利用基于融合告警和遗传算法的神经网络模型训练故障特征量定位模型矩阵并进行配网区段故障判别,通过算例分析证明,所提算法可以降低故障诊断的误差并提高容错率。

英文摘要:

In response to the unstable accuracy rate of fault location in distribution network lines and the significant impact of communication with distribution automation devices, the fault perception analysis of distribution network based on fusion alarm and genetic algorithm-BP neural network algorithm (FAGA-BP) is proposed in this paper. A fault localization and perception process is designed. Based on the alarm signal of the distribution automation device, a power loss alarm signal of the distribution transformer is introduced. A fusion alarm fault perception and localization model for the active distribution network is established, including the overcurrent alarm signal matrix of the protection action of the distribution automation device and the power loss alarm fault matrix of the distribution transformer. The selection of fault feature quantities and the fusion alarm rules are defined. The protection overcurrent alarm matrix and power loss alarm matrix are used to generate a fusion alarm fault feature quantity positioning model matrix using the definition rules. A fault type coding table is developed to characterize the fault type of the distribution network section. A neural network model based on fusion alarm and genetic algorithm is used to train the fault feature quantity positioning model matrix and perform fault discrimination in the distribution network section. Through case analysis, it is proven that the proposed algorithm can reduce the error of fault diagnosis and improve the fault tolerance rate.

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